Rationale of personalized immunosuppressive medication for hepatocellular carcinoma patients after liver transplantation
Bibliographic record
Abstract
Liver transplantation is the only potentially curative treatment for hepatocellular carcinoma (HCC) that is not eligible for surgical resection. However, disease recurrence is the main challenge to the success of this treatment. Immunosuppressants that are universally used after transplantation to prevent graft rejection could potentially have a significant impact on HCC recurrence. Nevertheless, current research is exclusively focused on mammalian target of rapamycin inhibitors, which are thought to be the only class of immunosuppressive agents that can reduce HCC recurrence. In fact, substantial evidence from the bench to the bedside indicates that other classes of immunosuppressants may also exert diverse effects; for example, inosine monophosphate dehydrogenase inhibitors potentially have antitumor effects. In this article, we aim to provide a comprehensive overview of the potential effects of different types of immunosuppressants on HCC recurrence and their mechanisms of action from both experimental and clinical perspectives. To ultimately improve the outcomes of HCC patients after transplantation, we propose a concept and approaches for developing personalized immunosuppressive medication to be used either as immunosuppression maintenance or during the prevention/treatment of HCC recurrence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".